{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sucess\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud\n",
    "import plotly as py\n",
    "from plotly.graph_objs import Scatter, Layout\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "from plotly.offline import download_plotlyjs,init_notebook_mode, plot, iplot\n",
    "py.offline.init_notebook_mode(connected=True)\n",
    "import colorlover as cl\n",
    "import matplotlib.pyplot as plt\n",
    "print('sucess')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>overall</th>\n",
       "      <th>teaching</th>\n",
       "      <th>research</th>\n",
       "      <th>citations</th>\n",
       "      <th>industry_income</th>\n",
       "      <th>international_outlook</th>\n",
       "      <th>number_students</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>University of Oxford</td>\n",
       "      <td>95.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>20298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>University of Cambridge</td>\n",
       "      <td>94.9</td>\n",
       "      <td>92.1</td>\n",
       "      <td>98.8</td>\n",
       "      <td>97.1</td>\n",
       "      <td>52.9</td>\n",
       "      <td>94.3</td>\n",
       "      <td>18749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Stanford University</td>\n",
       "      <td>95.2</td>\n",
       "      <td>93.6</td>\n",
       "      <td>96.8</td>\n",
       "      <td>99.9</td>\n",
       "      <td>64.6</td>\n",
       "      <td>79.3</td>\n",
       "      <td>15878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>94.5</td>\n",
       "      <td>91.9</td>\n",
       "      <td>92.7</td>\n",
       "      <td>99.9</td>\n",
       "      <td>87.6</td>\n",
       "      <td>89.0</td>\n",
       "      <td>11231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rank                                   name  overall  teaching  research  \\\n",
       "0    1                   University of Oxford     95.0      91.0      99.0   \n",
       "1    2                University of Cambridge     94.9      92.1      98.8   \n",
       "2    3                    Stanford University     95.2      93.6      96.8   \n",
       "3    4  Massachusetts Institute of Technology     94.5      91.9      92.7   \n",
       "\n",
       "   citations  industry_income  international_outlook  number_students  \n",
       "0       99.0             67.0                   96.0            20298  \n",
       "1       97.1             52.9                   94.3            18749  \n",
       "2       99.9             64.6                   79.3            15878  \n",
       "3       99.9             87.6                   89.0            11231  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni19=pd.read_csv('times2019.csv')\n",
    "uni19.iloc[0:4,0:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
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     "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "sucess\n"
     ]
    }
   ],
   "source": [
    "columns=['teaching','research','citations','income','intl_outlook']\n",
    "trace1=go.Scatterpolar(\n",
    "    r=uni19.iloc[0,3:8],\n",
    "    theta=columns,\n",
    "    fill='toself',\n",
    "    subplot='polar',\n",
    "    name=uni19['name'][0]\n",
    ")\n",
    "\n",
    "trace2=go.Scatterpolar(\n",
    "    r=uni19.iloc[1,3:8],\n",
    "    theta=columns,\n",
    "    fill='toself',\n",
    "    subplot='polar2',\n",
    "    name=uni19['name'][1]\n",
    ")\n",
    "\n",
    "trace3 = go.Scatterpolar(\n",
    "    r=uni19.iloc[2,3:8],\n",
    "    theta=columns,\n",
    "    fill='toself',\n",
    "    subplot='polar3',\n",
    "    name=uni19['name'][2]\n",
    ")\n",
    "\n",
    "trace4=go.Scatterpolar(\n",
    "    r=uni19.iloc[3,3:8],\n",
    "    theta=columns,\n",
    "    fill='toself',\n",
    "    subplot='polar4',\n",
    "    name=uni19['name'][3]\n",
    ")\n",
    "data=[trace1,trace2,trace3,trace4]\n",
    "\n",
    "layout=go.Layout(\n",
    "    title='排名前4的大学得分情况',\n",
    "    legend=dict(orientation=\"h\"),\n",
    "    polar = dict(domain = dict(x = [0, 0.45],y = [0.55, 1]),\n",
    "                 radialaxis = dict( visible = True, range = [0, 100])),\n",
    "    polar2 = dict(domain = dict(x = [0.55, 1],y = [0.55, 1]),\n",
    "                  radialaxis = dict( visible = True,range = [0, 100])),\n",
    "    polar3 = dict(domain = dict(x = [0, 0.45],y = [0, 0.45]),\n",
    "                  radialaxis = dict( visible = True,range = [0, 100])),\n",
    "    polar4 = dict(domain = dict(x = [0.55, 1],y = [0, 0.45]),\n",
    "                  radialaxis = dict( visible = True,range = [0, 100]))\n",
    ")\n",
    "fig = go.Figure(data=data,layout=layout)\n",
    "py.offline.iplot(fig,filename = \".\")\n",
    "#fig.show()\n",
    "print('sucess')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Top100\\u7684\\u4e94\\u5927\\u8bc4\\u5224\\u6807\\u51c6\"}, \"xaxis\": {\"domain\": [0, 0.45]}, \"xaxis2\": {\"domain\": [0.55, 1]}, \"xaxis3\": {\"anchor\": \"y3\", \"domain\": [0, 0.45]}, \"xaxis4\": {\"anchor\": \"y4\", \"domain\": [0.55, 1], \"title\": {\"text\": \"Overall\"}}, \"xaxis5\": {\"anchor\": \"y5\", \"domain\": [0, 0.45], \"title\": {\"text\": \"Overall\"}}, \"yaxis\": {\"domain\": [0.7, 1], \"title\": {\"text\": \"Teaching\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.7, 1], \"title\": {\"text\": \"Research\"}}, \"yaxis3\": {\"domain\": [0.35, 0.65], \"title\": {\"text\": \"Citations\"}}, \"yaxis4\": {\"anchor\": \"x4\", \"domain\": [0.35, 0.65], \"title\": {\"text\": \"Income\"}}, \"yaxis5\": {\"domain\": [0, 0.3], \"title\": {\"text\": \"International\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('fbb99406-5bee-46d8-8dec-70768d62ffc0');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace1=go.Scatter(\n",
    "    x=uni19['overall'][:100],\n",
    "    y=uni19['teaching'][:100],\n",
    "    mode='markers'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    x = uni19['overall'][:100],\n",
    "    y = uni19['research'][:100],\n",
    "    mode = 'markers', xaxis='x2',\n",
    "    yaxis='y2'\n",
    ")\n",
    "\n",
    "trace3 = go.Scatter(\n",
    "    x = uni19['overall'][:100],\n",
    "    y = uni19['citations'][:100],\n",
    "    mode = 'markers', xaxis='x3',\n",
    "    yaxis='y3'\n",
    ")\n",
    "\n",
    "trace4 = go.Scatter(\n",
    "    x = uni19['overall'][:100],\n",
    "    y = uni19['industry_income'][:100],\n",
    "    mode = 'markers', xaxis='x4',\n",
    "    yaxis='y4'\n",
    ")\n",
    "\n",
    "trace5 = go.Scatter(\n",
    "    x = uni19['overall'][:100],\n",
    "    y = uni19['international_outlook'][:100],\n",
    "    mode = 'markers', xaxis='x5',\n",
    "    yaxis='y5'\n",
    ")\n",
    "\n",
    "\n",
    "data = [trace1,trace2,trace3,trace4,trace5]\n",
    "\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        domain=[0, 0.45]\n",
    "    ),\n",
    "    yaxis=dict(title='Teaching',\n",
    "        domain=[0.7, 1]\n",
    "    ),\n",
    "    xaxis2=dict(\n",
    "        domain=[0.55, 1]\n",
    "    ),\n",
    "    xaxis3=dict(\n",
    "        domain=[0, 0.45],\n",
    "        anchor='y3'\n",
    "    ),\n",
    "    xaxis4=dict(title='Overall',\n",
    "        domain=[0.55, 1],\n",
    "        anchor='y4'\n",
    "    ),\n",
    "    xaxis5=dict(title='Overall',\n",
    "        domain=[0, 0.45],\n",
    "        anchor='y5'\n",
    "    ),\n",
    "    yaxis2=dict(title='Research',\n",
    "        domain=[0.7, 1],\n",
    "        anchor='x2'\n",
    "    ),\n",
    "    yaxis3=dict(title='Citations',\n",
    "        domain=[0.35, 0.65]\n",
    "    ),\n",
    "    yaxis4=dict(title='Income',\n",
    "        domain=[0.35, 0.65],\n",
    "        anchor='x4'\n",
    "    ),\n",
    "    yaxis5=dict(title='International',\n",
    "               domain=[0,0.3]),\n",
    "    showlegend=False,title='Top100的五大评判标准'\n",
    ")\n",
    "\n",
    "\n",
    "fig = go.Figure(data=data,layout=layout)\n",
    "py.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
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\"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Top100\\u56fd\\u9645\\u751f\\u6bd4\\u4f8b\\u4e0e\\u56fd\\u9645\\u5316\\u7a0b\\u5ea6\\u5f97\\u5206\"}, \"width\": 600, \"xaxis\": {\"title\": {\"text\": \"International outlook scores\"}}, \"yaxis\": {\"title\": {\"text\": \"International student \\uff08%\\uff09\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a352d5cf-005f-4c32-83b4-3d606f82fdd8');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace=go.Scatter(\n",
    "    y=uni19['intl_students'][:100],\n",
    "    x=uni19['international_outlook'][:100],\n",
    "    mode = 'markers'\n",
    ")\n",
    "data=[trace]\n",
    "layout=go.Layout(\n",
    "    title='Top100国际生比例与国际化程度得分',\n",
    "    width = 600,\n",
    "    height=400,\n",
    "    xaxis=dict(title='International outlook scores'),\n",
    "    yaxis=dict(title='International student （%）')\n",
    ")\n",
    "fig=go.Figure(data=data,layout=layout)\n",
    "py.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2240ff79cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jOLkb9mrEktlm69cjgJqq0Fhzf/i+fRZI+f/cnelQWcrmpdVUREimdCzbYWRi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\nXp2a28D2HTShEtFCxNQwXWaSb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dE4ndHENY9XFIVnugfY3dJJVDeI6iZCCM4UsvzrQz/h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\nRc6UXufQ4vcpevOo6MT19NKewFcP0G1V/gBI0u0ggpDcfImTh0b5pd98m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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22411451b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "words=dict()\n",
    "trunc_occurences = uni19.groupby('location').size().sort_values(ascending=False).reset_index(name='count')#index()\n",
    "trunc_occurences.columns = [\"location\", \"frequency\"]\n",
    "#trunc_occurences['location'][0]\n",
    "for i in range(18):\n",
    "    words[trunc_occurences['location'][i]] =trunc_occurences['frequency'][i]\n",
    "tone=100\n",
    "f,ax=plt.subplots(figsize=(14,6))\n",
    "wordcloud = WordCloud(width=550,height=300, background_color='white', \n",
    "                      max_words=1628,relative_scaling=0.7,\n",
    "                      normalize_plurals=False)\n",
    "wordcloud.generate_from_frequencies(words)\n",
    "plt.imshow(wordcloud,interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "marker": {
          "color": [
           "rgb(166,206,227)",
           "rgb(31,120,180)",
           "rgb(178,223,138)",
           "rgb(51,160,44)",
           "rgb(251,154,153)",
           "rgb(227,26,28)",
           "rgb(253,191,111)",
           "rgb(255,127,0)",
           "rgb(202,178,214)",
           "rgb(106,61,154)",
           "rgb(255,255,153)",
           "rgb(177,89,40)"
          ]
         },
         "text": [
          41,
          11,
          8,
          7,
          6,
          5,
          3,
          3,
          3,
          3,
          2,
          2,
          2,
          2,
          1,
          1
         ],
         "textposition": "outside",
         "type": "bar",
         "x": [
          "United States",
          "United Kingdom",
          "Germany",
          "Netherlands",
          "Australia",
          "Canada",
          "Switzerland",
          "Sweden",
          "Hong Kong",
          "China",
          "South Korea",
          "Singapore",
          "Japan",
          "France",
          "Finland",
          "Belgium"
         ],
         "y": [
          41,
          11,
          8,
          7,
          6,
          5,
          3,
          3,
          3,
          3,
          2,
          2,
          2,
          2,
          1,
          1
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
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       "var gd = document.getElementById('6f973ac2-1730-4680-ad58-162052a35af1');\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "var outputEl = gd.closest('.output');\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count100 = uni19.head(100).groupby('location').count()['name'].sort_values(ascending=False)\n",
    "data=[go.Bar(x=count100.index,y=count100,text=count100,textposition='outside',marker=dict(color = cl.scales['12']['qual']['Paired']))]\n",
    "layout=go.Layout(title='前100名大学所在地区')\n",
    "fig=go.Figure(data=data,layout=layout)\n",
    "py.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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